In this paper,we aim to unlock the potential of intelligent reflecting surfaces(IRSs)in cognitive internet of things(loT).Considering that the secondary IoT devices send messages to the secondary access point(SAP)by s...In this paper,we aim to unlock the potential of intelligent reflecting surfaces(IRSs)in cognitive internet of things(loT).Considering that the secondary IoT devices send messages to the secondary access point(SAP)by sharing the spectrum with the primary network,the interference is introduced by the IoT devices to the primary access point(PAP)which profits from the IoT devices by pricing the interference power charged by them.A practical path loss model is adopted such that the IRSs deployed between the IoT devices and SAP serve as diffuse scatterers,but each reflected signal can be aligned with its own desired direction.Moreover,two transmission policies of the secondary network are investigated without/with a successive interference cancellation(SIC)technique.The signal-to-interference plus noise ratio(SINR)balancing is considered to overcome the nearfar effect of the IoT devices so as to allocate the resource fairly among them.We propose a Stackelberg game strategy to characterize the interaction between primary and secondary networks.For the proposed game,the Stackelberg equilibrium is analytically derived to optimally obtain the closed-form solution of the power allocation and interference pricing.Numerical results are demonstrated to validate the performance of the theoretical derivations.展开更多
The wide variety of smart embedded computing devices and their increasing number of applications in our daily life have created new op- portunities to acquire knowledge from the physical world anytime and anywhere, wh...The wide variety of smart embedded computing devices and their increasing number of applications in our daily life have created new op- portunities to acquire knowledge from the physical world anytime and anywhere, which is envisioned as the＂Internet of Things＂ （IoT）. Since a huge number of heterogeneous resources are brought in- to IoT, one of the main challenges is how to effi- ciently manage the increasing complexity of IoT in a scalable, flexNle, and autonomic way. Further- more, the emerging IoT applications will require collaborations among loosely coupled devices, which may reside in various locations of the Inter- net. In this paper, we propose a new IoT network management architecture based on cognitive net- work management technology and Service-Orien- ted Architecture to provide effective and efficient network management of loT.展开更多
Forecasting the weather is a challenging task for human beings because of the unpredictable nature of the climate.However,effective forecasting is vital for the general growth of a country due to the significance of w...Forecasting the weather is a challenging task for human beings because of the unpredictable nature of the climate.However,effective forecasting is vital for the general growth of a country due to the significance of weather forecasting in science and technology.The primary motivation behind this work is to achieve a higher level of forecasting accuracy to avoid any damage.Currently,most weather forecasting work is based on initially observed numerical weather data that cannot fully cover the changing essence of the atmosphere.In this work,sensors are used to collect real-time data for a particular location to capture the varying nature of the atmosphere.Our solution can give the anticipated results with the least amount of human engagement by combining human intelligence and machine learning with the help of the cognitive Internet of Things.The Authors identified weatherrelated parameters such as temperature,humidity,wind speed,and rainfall and then applied cognitive data collection methods to train and validate their findings.In addition,the Authors have examined the efficacy of various machine learning algorithms by using them on both data sets i.e.,pre-recorded metrological data sets and live sensor data sets collected from multiple locations.The Authors noticed that the results were superior on the sensor data.The Authors developed ensemble learning model using stacked method that achieved 99.25%accuracy,99%recall,99%precision,and 99%F1-score for Sensor data.It also achieved 85%accuracy,86%recall,85%precision,and 86%F1 score for Australian rainfall data.展开更多
Unquestionably, communicating entities （object, or things） in the Internet of Things （IoT） context are playing an active role in human activities, systems and processes. The high connectivity of intelligent object...Unquestionably, communicating entities （object, or things） in the Internet of Things （IoT） context are playing an active role in human activities, systems and processes. The high connectivity of intelligent objects and their severe constraints lead to many security challenges, which are not included in the classical formulation of security problems and solutions. The Security Shield for IoT has been identified by DARPA （Defense Advanced Research Projects Agency） as one of the four projects with a potential impact broader than the Internet itself. To help interested researchers contribute to this research area, an overview of the loT security roadmap overview is presented in this paper based on a novel cognitive and systemic approach. The role of each component of the approach is explained, we also study its interactions with the other main components, and their impact on the overall. A case study is presented to highlight the components and interactions of the systemic and cognitive approach. Then, security questions about privacy, trust, identification, and access control are discussed. According to the novel taxonomy of the loT framework, different research challenges are highlighted, important solutions and research activities are revealed, and interesting research directions are proposed. In addition, current stan dardization activities are surveyed and discussed to the ensure the security of loT components and applications.展开更多
In the last few years, the number of devices operating in wireless Internet of Things （IoT） has experienced tremendous growth. On the other hand, the growth results in spectrum scarcity. Cog- nitive Radio （CR） sys...In the last few years, the number of devices operating in wireless Internet of Things （IoT） has experienced tremendous growth. On the other hand, the growth results in spectrum scarcity. Cog- nitive Radio （CR） systems have been proposed to efficiently exploit the spectra that have been assigned but are underutilized. In this paper, a spectrum sensing model based on Markov chain is proposed to predict the spectrum hole for CR in wireless IoT. Theoretical analysis and simulation results have been evaluated that a Markov model with two- state or four-state works well enough in wireless loT whereas a model with more states is not necessary for it is complex.展开更多
Recently,the Internet of Medical Things(IoMT)has become a research hotspot due to its various applicability in medical field.However,the data analysis and management in IoMT remain challenging owing to the existence o...Recently,the Internet of Medical Things(IoMT)has become a research hotspot due to its various applicability in medical field.However,the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment,generating a massive quantity of healthcare data.In such cases,cognitive computing can be employed that uses many intelligent technologies-machine learning(ML),deep learning(DL),artificial intelligence(AI),natural language processing(NLP)and others-to comprehend data expansively.Furthermore,breast cancer(BC)has been found to be a major cause of mortality among ladies globally.Earlier detection and classification of BC using digital mammograms can decrease the mortality rate.This paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm(DLMOMFO)for BC diagnosis and classification in the IoMT environment.The goal of this paper is to integrate deep learning(DL)and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify BC.The proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter(AWMF)-based noise removal and contrast-limited adaptive histogram equalisation(CLAHE)-based contrast improvement techniques to improve the quality of the digital mammograms.In addition,a U-Net architecture-based segmentation method was utilised to detect diseased regions in the mammograms.Moreover,a SqueezeNet-based feature extraction and a fuzzy support vector machine(FSVM)classifier were used in the presented technique.To enhance the diagnostic performance of the presented method,the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM techniques.The DL-MOMFO technique was tested on the MIAS database,and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.展开更多
The exponential growth of the Internet of Things(IoT)has revolutionized various domains such as healthcare,smart cities,and agriculture,generating vast volumes of data that require secure processing and storage in clo...The exponential growth of the Internet of Things(IoT)has revolutionized various domains such as healthcare,smart cities,and agriculture,generating vast volumes of data that require secure processing and storage in cloud environments.However,reliance on cloud infrastructure raises critical security challenges,particularly regarding data integrity.While existing cryptographic methods provide robust integrity verification,they impose significant computational and energy overheads on resource-constrained IoT devices,limiting their applicability in large-scale,real-time scenarios.To address these challenges,we propose the Cognitive-Based Integrity Verification Model(C-BIVM),which leverages Belief-Desire-Intention(BDI)cognitive intelligence and algebraic signatures to enable lightweight,efficient,and scalable data integrity verification.The model incorporates batch auditing,reducing resource consumption in large-scale IoT environments by approximately 35%,while achieving an accuracy of over 99.2%in detecting data corruption.C-BIVM dynamically adapts integrity checks based on real-time conditions,optimizing resource utilization by minimizing redundant operations by more than 30%.Furthermore,blind verification techniques safeguard sensitive IoT data,ensuring privacy compliance by preventing unauthorized access during integrity checks.Extensive experimental evaluations demonstrate that C-BIVM reduces computation time for integrity checks by up to 40%compared to traditional bilinear pairing-based methods,making it particularly suitable for IoT-driven applications in smart cities,healthcare,and beyond.These results underscore the effectiveness of C-BIVM in delivering a secure,scalable,and resource-efficient solution tailored to the evolving needs of IoT ecosystems.展开更多
The Internet of Things （loT） is called the world＇ s third wave of the information industry. As the core technology of IoT, Cognitive Radio Sensor Networks （CRSN） technology can improve spectrum utilization effici...The Internet of Things （loT） is called the world＇ s third wave of the information industry. As the core technology of IoT, Cognitive Radio Sensor Networks （CRSN） technology can improve spectrum utilization efficiency and lay a sofid foundation for large-scale application of IoT. Reliable spectrum sensing is a crucial task of the CR. For energy de- tection, threshold will determine the probability of detection （Pd） and the probability of false alarm Pf at the same time. While the threshold increases, Pd and Pf will both decrease. In this paper we focus on the maximum of the difference of Pd and Pf, and try to find out how to determine the threshold with this precondition. Simulation results show that the proposed method can effectively approach the ideal optimal result.展开更多
Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the...Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the healthcare sector to avail effective decision making.On the other hand,Alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare professional.In this aspect,this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features(BRIEF)Detector(ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare system.For initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest(RoI)detection processes are carried out.In addition,the ORBOANN model includes ORB based feature extractor and principal component analysis(PCA)based feature selector.Moreover,artificial neural network(ANN)model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.展开更多
Recently,Cooperative Spectrum Sensing(CSS)for Cognitive Radio Networks(CRN)plays a significant role in efficient 5G wireless communication.Spectrum sensing is a significant technology in CRN to identify underutilized ...Recently,Cooperative Spectrum Sensing(CSS)for Cognitive Radio Networks(CRN)plays a significant role in efficient 5G wireless communication.Spectrum sensing is a significant technology in CRN to identify underutilized spectrums.The CSS technique is highly applicable due to its fast and efficient performance.5G wireless communication is widely employed for the continuous development of efficient and accurate Internet of Things(IoT)networks.5G wireless communication will potentially lead the way for next generation IoT communication.CSS has established significant consideration as a feasible resource to improve identification performance by developing spatial diversity in receiving signal strength in IoT.In this paper,an optimal CSS for CRN is performed using Offset Quadrature Amplitude Modulation Universal Filtered Multi-Carrier Non-Orthogonal Multiple Access(OQAM/UFMC/NOMA)methodologies.Availability of spectrum and bandwidth utilization is a key challenge in CRN for IoT 5G wireless communication.The optimal solution for CRN in IoT-based 5G communication should be able to provide optimal bandwidth and CSS,low latency,Signal Noise Ratio(SNR)improvement,maximum capacity,offset synchronization,and Peak Average Power Ratio(PAPR)reduction.The Energy Efficient All-Pass Filter(EEAPF)algorithm is used to eliminate PAPR.The deployment approach improves Quality of Service(QoS)in terms of system reliability,throughput,and energy efficiency.Our in-depth experimental results show that the proposed methodology provides an optimal solution when directly compares against current existing methodologies.展开更多
Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to...Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless systems.In this work,a cooperative CIoT system is contemplated,in which a source acts as a satellite,communicating with multiple CIoT devices over numerous relays.Unmanned Aerial Vehicles(UAVs)are used as relays,which are equipped with onboard Energy Harvesting(EH)facility.We adopted a Power Splitting(PS)method for EH at relays,which are harvested from the Radio frequency(RF)signals.In conjunction with this,the Decode and Forward(DF)relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT devices.We developed a Multi-Objective Optimization(MOO)framework for joint optimization of source power allocation,CIoT device selection,UAV relay assignment,and PS ratio determination.We formulated three objectives:maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide emission.The MOO formulation is a Mixed-Integer Non-Linear Programming(MINLP)problem,which is challenging to solve.To address the joint optimization problem for an epsilon optimal solution,an Outer Approximation Algorithm(OAA)is proposed with reduced complexity.The simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search(NOMAD)algorithm.展开更多
智慧城市旨在提高城市管理效率,改善市民生活质量。作为智慧城市的重要元素,大量物联网(Internet of things,IoT)设备的接入对实时性数据和资源管理提出了更高要求。然而,各实体之间数据共享不足,数据“孤岛”现象普遍存在,成为智慧城...智慧城市旨在提高城市管理效率,改善市民生活质量。作为智慧城市的重要元素,大量物联网(Internet of things,IoT)设备的接入对实时性数据和资源管理提出了更高要求。然而,各实体之间数据共享不足,数据“孤岛”现象普遍存在,成为智慧城市深入发展的障碍。数字孪生(digitaltwin,DT)作为一种新兴的通信模式,具有消除智慧城市中数据共享障碍的潜力。提出了一种嵌入式认知无线电(cognitive radio,CR)辅助的非正交多址接入(NOMA)(CR_NOMA)系统频谱资源分配方案,在智慧城市数字孪生网络中实现无障碍数据共享。首先,提出一种新颖的CR模式,以嵌入方式使用频谱空穴,即允许次用户接入主用户释放的频谱空穴而不对其他活跃主用户造成额外干扰;其次,针对信道老化现象进行信道预测,以改善系统性能下降问题;最后,设计基于在线学习的频谱调度方案,借助孪生体之间数据共享的先天优势,实现实时资源调度。仿真结果表明,所提方案的性能显著优于现有的CR_NOMA和NOMA方法。在等同资源块长情况下,系统和速率较CR_NOMA方法提升66%,而较传统NOMA方法提升103%。尤其当设备处于运动状态时,性能提升更为显著。展开更多
Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading...Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading to poor performance and privacy breaches.Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business development.To ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)model.However,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the cloud.HD2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of Authority.The“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to poisoning.FL with Blockchain allows quick consensus through smart member selection and verification.The HD2C paradigm significantly improves the computational processing efficiency of intelligent manufacturing.Extensive analysis results derived from IIoT datasets confirm HD2C superiority.When compared to other consensus algorithms,the Blockchain PoA’s foundational cost is significant.The accuracy and memory utilization evaluation results predict the total benefits of the system.In comparison to the values 0.004 and 0.04,the value of 0.4 achieves good accuracy.According to the experiment results,the number of transactions per second has minimal impact on memory requirements.The findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.展开更多
Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response.Cognitive Internet of things(CIoT)technologies including...Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response.Cognitive Internet of things(CIoT)technologies including inherent characteristics of cognitive radio(CR)are potential candidates to develop a monitoring and early warning system(MEWS)that helps in efficiently utilizing the short response time to save lives during flash floods.However,most CIoT devices are battery-limited and thus,it reduces the lifetime of the MEWS.To tackle these problems,we propose a CIoTbased MEWS to slash the fatalities of flash floods.To extend the lifetime of the MEWS by conserving the limited battery energy of CIoT sensors,we formulate a resource assignment problem for maximizing energy efficiency.To solve the problem,at first,we devise a polynomial-time heuristic energyefficient scheduler(EES-1).However,its performance can be unsatisfactory since it requires an exhaustive search to find local optimum values without consideration of the overall network energy efficiency.To enhance the energy efficiency of the proposed EES-1 scheme,we additionally formulate an optimization problem based on a maximum weight matching bipartite graph.Then,we additionally propose a Hungarian algorithm-based energy-efficient scheduler(EES-2),solvable in polynomial time.The simulation results show that the proposed EES-2 scheme achieves considerably high energy efficiency in the CIoT-based MEWS,leading to the extended lifetime of the MEWS without loss of throughput performance.展开更多
Cognitive Internet of Things(IoT)has at-tracted much attention due to its high spectrum uti-lization.However,potential security of the short-packet communications in cognitive IoT becomes an important issue.This paper...Cognitive Internet of Things(IoT)has at-tracted much attention due to its high spectrum uti-lization.However,potential security of the short-packet communications in cognitive IoT becomes an important issue.This paper proposes a relay-assisted maximum ratio combining/zero forcing beamforming(MRC/ZFB)scheme to guarantee the secrecy perfor-mance of dual-hop short-packet communications in cognitive IoT.This paper analyzes the average secrecy throughput of the system and further investigates two asymptotic scenarios with the high signal-to-noise ra-tio(SNR)regime and the infinite blocklength.In ad-dition,the Fibonacci-based alternating optimization method is adopted to jointly optimize the spectrum sensing blocklength and transmission blocklength to maximize the average secrecy throughput.The nu-merical results verify the impact of the system pa-rameters on the tradeoff between the spectrum sensing blocklength and transmission blocklength under a se-crecy constraint.It is shown that the proposed scheme achieves better secrecy performance than other bench-mark schemes.展开更多
基金This work was supported by the U.K.Engineering and Physical Sciences Research Council under Grants EP/P008402/2 and EP/R001588/1.
文摘In this paper,we aim to unlock the potential of intelligent reflecting surfaces(IRSs)in cognitive internet of things(loT).Considering that the secondary IoT devices send messages to the secondary access point(SAP)by sharing the spectrum with the primary network,the interference is introduced by the IoT devices to the primary access point(PAP)which profits from the IoT devices by pricing the interference power charged by them.A practical path loss model is adopted such that the IRSs deployed between the IoT devices and SAP serve as diffuse scatterers,but each reflected signal can be aligned with its own desired direction.Moreover,two transmission policies of the secondary network are investigated without/with a successive interference cancellation(SIC)technique.The signal-to-interference plus noise ratio(SINR)balancing is considered to overcome the nearfar effect of the IoT devices so as to allocate the resource fairly among them.We propose a Stackelberg game strategy to characterize the interaction between primary and secondary networks.For the proposed game,the Stackelberg equilibrium is analytically derived to optimally obtain the closed-form solution of the power allocation and interference pricing.Numerical results are demonstrated to validate the performance of the theoretical derivations.
基金supported by the National Sci.&Tech. Major Project of China(No.2010ZX03004-002)the National Natural Science Foundation of China(No.60972083)
文摘The wide variety of smart embedded computing devices and their increasing number of applications in our daily life have created new op- portunities to acquire knowledge from the physical world anytime and anywhere, which is envisioned as the＂Internet of Things＂ （IoT）. Since a huge number of heterogeneous resources are brought in- to IoT, one of the main challenges is how to effi- ciently manage the increasing complexity of IoT in a scalable, flexNle, and autonomic way. Further- more, the emerging IoT applications will require collaborations among loosely coupled devices, which may reside in various locations of the Inter- net. In this paper, we propose a new IoT network management architecture based on cognitive net- work management technology and Service-Orien- ted Architecture to provide effective and efficient network management of loT.
文摘Forecasting the weather is a challenging task for human beings because of the unpredictable nature of the climate.However,effective forecasting is vital for the general growth of a country due to the significance of weather forecasting in science and technology.The primary motivation behind this work is to achieve a higher level of forecasting accuracy to avoid any damage.Currently,most weather forecasting work is based on initially observed numerical weather data that cannot fully cover the changing essence of the atmosphere.In this work,sensors are used to collect real-time data for a particular location to capture the varying nature of the atmosphere.Our solution can give the anticipated results with the least amount of human engagement by combining human intelligence and machine learning with the help of the cognitive Internet of Things.The Authors identified weatherrelated parameters such as temperature,humidity,wind speed,and rainfall and then applied cognitive data collection methods to train and validate their findings.In addition,the Authors have examined the efficacy of various machine learning algorithms by using them on both data sets i.e.,pre-recorded metrological data sets and live sensor data sets collected from multiple locations.The Authors noticed that the results were superior on the sensor data.The Authors developed ensemble learning model using stacked method that achieved 99.25%accuracy,99%recall,99%precision,and 99%F1-score for Sensor data.It also achieved 85%accuracy,86%recall,85%precision,and 86%F1 score for Australian rainfall data.
文摘Unquestionably, communicating entities （object, or things） in the Internet of Things （IoT） context are playing an active role in human activities, systems and processes. The high connectivity of intelligent objects and their severe constraints lead to many security challenges, which are not included in the classical formulation of security problems and solutions. The Security Shield for IoT has been identified by DARPA （Defense Advanced Research Projects Agency） as one of the four projects with a potential impact broader than the Internet itself. To help interested researchers contribute to this research area, an overview of the loT security roadmap overview is presented in this paper based on a novel cognitive and systemic approach. The role of each component of the approach is explained, we also study its interactions with the other main components, and their impact on the overall. A case study is presented to highlight the components and interactions of the systemic and cognitive approach. Then, security questions about privacy, trust, identification, and access control are discussed. According to the novel taxonomy of the loT framework, different research challenges are highlighted, important solutions and research activities are revealed, and interesting research directions are proposed. In addition, current stan dardization activities are surveyed and discussed to the ensure the security of loT components and applications.
基金supported by the Fundamental Research Funds for the Central UniversitiesSpecial Funds for Key Program of the China(2009ZX01039-002-001-07)+2 种基金Natural Science Foundation of China(Nos.60971082and61872049)National Great Science Specific Project(2010ZX03005-001-03)Beijing Municipal Commission of Education Build Together Project and Ministry of Education Infrastructure Construction Project(2-5-2)
文摘In the last few years, the number of devices operating in wireless Internet of Things （IoT） has experienced tremendous growth. On the other hand, the growth results in spectrum scarcity. Cog- nitive Radio （CR） systems have been proposed to efficiently exploit the spectra that have been assigned but are underutilized. In this paper, a spectrum sensing model based on Markov chain is proposed to predict the spectrum hole for CR in wireless IoT. Theoretical analysis and simulation results have been evaluated that a Markov model with two- state or four-state works well enough in wireless loT whereas a model with more states is not necessary for it is complex.
基金We deeply acknowledge Taif University for supporting this study through Taif University Researchers Supporting Project Number(TURSP-2020/328),Taif University,Taif,Saudi Arabia.
文摘Recently,the Internet of Medical Things(IoMT)has become a research hotspot due to its various applicability in medical field.However,the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment,generating a massive quantity of healthcare data.In such cases,cognitive computing can be employed that uses many intelligent technologies-machine learning(ML),deep learning(DL),artificial intelligence(AI),natural language processing(NLP)and others-to comprehend data expansively.Furthermore,breast cancer(BC)has been found to be a major cause of mortality among ladies globally.Earlier detection and classification of BC using digital mammograms can decrease the mortality rate.This paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm(DLMOMFO)for BC diagnosis and classification in the IoMT environment.The goal of this paper is to integrate deep learning(DL)and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify BC.The proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter(AWMF)-based noise removal and contrast-limited adaptive histogram equalisation(CLAHE)-based contrast improvement techniques to improve the quality of the digital mammograms.In addition,a U-Net architecture-based segmentation method was utilised to detect diseased regions in the mammograms.Moreover,a SqueezeNet-based feature extraction and a fuzzy support vector machine(FSVM)classifier were used in the presented technique.To enhance the diagnostic performance of the presented method,the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM techniques.The DL-MOMFO technique was tested on the MIAS database,and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.
基金supported by King Saud University,Riyadh,Saudi Arabia,through Researchers Supporting Project number RSP2025R498.
文摘The exponential growth of the Internet of Things(IoT)has revolutionized various domains such as healthcare,smart cities,and agriculture,generating vast volumes of data that require secure processing and storage in cloud environments.However,reliance on cloud infrastructure raises critical security challenges,particularly regarding data integrity.While existing cryptographic methods provide robust integrity verification,they impose significant computational and energy overheads on resource-constrained IoT devices,limiting their applicability in large-scale,real-time scenarios.To address these challenges,we propose the Cognitive-Based Integrity Verification Model(C-BIVM),which leverages Belief-Desire-Intention(BDI)cognitive intelligence and algebraic signatures to enable lightweight,efficient,and scalable data integrity verification.The model incorporates batch auditing,reducing resource consumption in large-scale IoT environments by approximately 35%,while achieving an accuracy of over 99.2%in detecting data corruption.C-BIVM dynamically adapts integrity checks based on real-time conditions,optimizing resource utilization by minimizing redundant operations by more than 30%.Furthermore,blind verification techniques safeguard sensitive IoT data,ensuring privacy compliance by preventing unauthorized access during integrity checks.Extensive experimental evaluations demonstrate that C-BIVM reduces computation time for integrity checks by up to 40%compared to traditional bilinear pairing-based methods,making it particularly suitable for IoT-driven applications in smart cities,healthcare,and beyond.These results underscore the effectiveness of C-BIVM in delivering a secure,scalable,and resource-efficient solution tailored to the evolving needs of IoT ecosystems.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.60971082,60872049,60972073and60871042)the National Key Basic Research Program of China(Grant No.2009CB320400)+1 种基金the National Great Science Specific Project(Grant Nos.2009ZX03003-001,2009ZX03003-011and2010ZX03001003)Chinese Universities Scientific Fund,China
文摘The Internet of Things （loT） is called the world＇ s third wave of the information industry. As the core technology of IoT, Cognitive Radio Sensor Networks （CRSN） technology can improve spectrum utilization efficiency and lay a sofid foundation for large-scale application of IoT. Reliable spectrum sensing is a crucial task of the CR. For energy de- tection, threshold will determine the probability of detection （Pd） and the probability of false alarm Pf at the same time. While the threshold increases, Pd and Pf will both decrease. In this paper we focus on the maximum of the difference of Pd and Pf, and try to find out how to determine the threshold with this precondition. Simulation results show that the proposed method can effectively approach the ideal optimal result.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/23/42),Received by Fahd N.Al-Wesabi.www.kku.edu.sa.
文摘Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the healthcare sector to avail effective decision making.On the other hand,Alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare professional.In this aspect,this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features(BRIEF)Detector(ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare system.For initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest(RoI)detection processes are carried out.In addition,the ORBOANN model includes ORB based feature extractor and principal component analysis(PCA)based feature selector.Moreover,artificial neural network(ANN)model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.
文摘Recently,Cooperative Spectrum Sensing(CSS)for Cognitive Radio Networks(CRN)plays a significant role in efficient 5G wireless communication.Spectrum sensing is a significant technology in CRN to identify underutilized spectrums.The CSS technique is highly applicable due to its fast and efficient performance.5G wireless communication is widely employed for the continuous development of efficient and accurate Internet of Things(IoT)networks.5G wireless communication will potentially lead the way for next generation IoT communication.CSS has established significant consideration as a feasible resource to improve identification performance by developing spatial diversity in receiving signal strength in IoT.In this paper,an optimal CSS for CRN is performed using Offset Quadrature Amplitude Modulation Universal Filtered Multi-Carrier Non-Orthogonal Multiple Access(OQAM/UFMC/NOMA)methodologies.Availability of spectrum and bandwidth utilization is a key challenge in CRN for IoT 5G wireless communication.The optimal solution for CRN in IoT-based 5G communication should be able to provide optimal bandwidth and CSS,low latency,Signal Noise Ratio(SNR)improvement,maximum capacity,offset synchronization,and Peak Average Power Ratio(PAPR)reduction.The Energy Efficient All-Pass Filter(EEAPF)algorithm is used to eliminate PAPR.The deployment approach improves Quality of Service(QoS)in terms of system reliability,throughput,and energy efficiency.Our in-depth experimental results show that the proposed methodology provides an optimal solution when directly compares against current existing methodologies.
文摘Cooperative communication through energy harvested relays in Cognitive Internet of Things(CIoT)has been envisioned as a promising solution to support massive connectivity of Cognitive Radio(CR)based IoT devices and to achieve maximal energy and spectral efficiency in upcoming wireless systems.In this work,a cooperative CIoT system is contemplated,in which a source acts as a satellite,communicating with multiple CIoT devices over numerous relays.Unmanned Aerial Vehicles(UAVs)are used as relays,which are equipped with onboard Energy Harvesting(EH)facility.We adopted a Power Splitting(PS)method for EH at relays,which are harvested from the Radio frequency(RF)signals.In conjunction with this,the Decode and Forward(DF)relaying strategy is used at UAV relays to transmit the messages from the satellite source to the CIoT devices.We developed a Multi-Objective Optimization(MOO)framework for joint optimization of source power allocation,CIoT device selection,UAV relay assignment,and PS ratio determination.We formulated three objectives:maximizing the sum rate and the number of admitted CIoT in the network and minimizing the carbon dioxide emission.The MOO formulation is a Mixed-Integer Non-Linear Programming(MINLP)problem,which is challenging to solve.To address the joint optimization problem for an epsilon optimal solution,an Outer Approximation Algorithm(OAA)is proposed with reduced complexity.The simulation results show that the proposed OAA is superior in terms of CIoT device selection and network utility maximization when compared to those obtained using the Nonlinear Optimization with Mesh Adaptive Direct-search(NOMAD)algorithm.
文摘智慧城市旨在提高城市管理效率,改善市民生活质量。作为智慧城市的重要元素,大量物联网(Internet of things,IoT)设备的接入对实时性数据和资源管理提出了更高要求。然而,各实体之间数据共享不足,数据“孤岛”现象普遍存在,成为智慧城市深入发展的障碍。数字孪生(digitaltwin,DT)作为一种新兴的通信模式,具有消除智慧城市中数据共享障碍的潜力。提出了一种嵌入式认知无线电(cognitive radio,CR)辅助的非正交多址接入(NOMA)(CR_NOMA)系统频谱资源分配方案,在智慧城市数字孪生网络中实现无障碍数据共享。首先,提出一种新颖的CR模式,以嵌入方式使用频谱空穴,即允许次用户接入主用户释放的频谱空穴而不对其他活跃主用户造成额外干扰;其次,针对信道老化现象进行信道预测,以改善系统性能下降问题;最后,设计基于在线学习的频谱调度方案,借助孪生体之间数据共享的先天优势,实现实时资源调度。仿真结果表明,所提方案的性能显著优于现有的CR_NOMA和NOMA方法。在等同资源块长情况下,系统和速率较CR_NOMA方法提升66%,而较传统NOMA方法提升103%。尤其当设备处于运动状态时,性能提升更为显著。
文摘Cloud storage is widely used by large companies to store vast amounts of data and files,offering flexibility,financial savings,and security.However,information shoplifting poses significant threats,potentially leading to poor performance and privacy breaches.Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms,ensuring businesses can focus on business development.To ensure data security in cloud platforms,this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing(HD2C)model.However,the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things(IoT)in the cloud.HD2C is developed by combining Federated Learning(FL)with a Blockchain consensus algorithm to connect smart contracts with Proof of Authority.The“Data Island”problem can be solved by FL’s emphasis on privacy and lightning-fast processing,while Blockchain provides a decentralized incentive structure that is impervious to poisoning.FL with Blockchain allows quick consensus through smart member selection and verification.The HD2C paradigm significantly improves the computational processing efficiency of intelligent manufacturing.Extensive analysis results derived from IIoT datasets confirm HD2C superiority.When compared to other consensus algorithms,the Blockchain PoA’s foundational cost is significant.The accuracy and memory utilization evaluation results predict the total benefits of the system.In comparison to the values 0.004 and 0.04,the value of 0.4 achieves good accuracy.According to the experiment results,the number of transactions per second has minimal impact on memory requirements.The findings of this study resulted in the development of a brand-new IIoT framework based on blockchain technology.
基金This work was supported in part by the Ministry of Science and ICT(MSIT)Korea,under the Information and Technology Research Center(ITRC)support program(IITP-2021-2018-0-01426)in part by the National Research Foundation of Korea(NRF)funded by the Korea government(MSIT)(No.2019R1F1A1059125).
文摘Flash floods are deemed the most fatal and disastrous natural hazards globally due to their prompt onset that requires a short prime time for emergency response.Cognitive Internet of things(CIoT)technologies including inherent characteristics of cognitive radio(CR)are potential candidates to develop a monitoring and early warning system(MEWS)that helps in efficiently utilizing the short response time to save lives during flash floods.However,most CIoT devices are battery-limited and thus,it reduces the lifetime of the MEWS.To tackle these problems,we propose a CIoTbased MEWS to slash the fatalities of flash floods.To extend the lifetime of the MEWS by conserving the limited battery energy of CIoT sensors,we formulate a resource assignment problem for maximizing energy efficiency.To solve the problem,at first,we devise a polynomial-time heuristic energyefficient scheduler(EES-1).However,its performance can be unsatisfactory since it requires an exhaustive search to find local optimum values without consideration of the overall network energy efficiency.To enhance the energy efficiency of the proposed EES-1 scheme,we additionally formulate an optimization problem based on a maximum weight matching bipartite graph.Then,we additionally propose a Hungarian algorithm-based energy-efficient scheduler(EES-2),solvable in polynomial time.The simulation results show that the proposed EES-2 scheme achieves considerably high energy efficiency in the CIoT-based MEWS,leading to the extended lifetime of the MEWS without loss of throughput performance.
基金Natural Science Foun-dation of China(No.62171464,61801496 and 61771487)This paper was presented in part at the 2021 IEEE International Conference on Communica-tions Workshops(ICC Workshops),2021.
文摘Cognitive Internet of Things(IoT)has at-tracted much attention due to its high spectrum uti-lization.However,potential security of the short-packet communications in cognitive IoT becomes an important issue.This paper proposes a relay-assisted maximum ratio combining/zero forcing beamforming(MRC/ZFB)scheme to guarantee the secrecy perfor-mance of dual-hop short-packet communications in cognitive IoT.This paper analyzes the average secrecy throughput of the system and further investigates two asymptotic scenarios with the high signal-to-noise ra-tio(SNR)regime and the infinite blocklength.In ad-dition,the Fibonacci-based alternating optimization method is adopted to jointly optimize the spectrum sensing blocklength and transmission blocklength to maximize the average secrecy throughput.The nu-merical results verify the impact of the system pa-rameters on the tradeoff between the spectrum sensing blocklength and transmission blocklength under a se-crecy constraint.It is shown that the proposed scheme achieves better secrecy performance than other bench-mark schemes.
